class_docstr: DBNet as described in `"Real-time Scene Text Detection with Differentiable Binarization"
  <https://arxiv.org/pdf/1911.08947.pdf>`_.
  
  Args:
  ----
      feature extractor: the backbone serving as feature extractor
      fpn_channels: number of channels each extracted feature maps is mapped to
      assume_straight_pages: if True, fit straight bounding boxes only
      exportable: onnx exportable returns only logits
      cfg: the configuration dict of the model
      class_names: list of class names
code: def call(
      self,
      x: tf.Tensor,
      target: Optional[List[Dict[str, np.ndarray]]] = None,
      return_model_output: bool = False,
      return_preds: bool = False,
      **kwargs: Any,
  ) -> Dict[str, Any]:
      feat_maps = self.feat_extractor(x, **kwargs)
      feat_concat = self.fpn(feat_maps, **kwargs)
      logits = self.probability_head(feat_concat, **kwargs)
  
      out: Dict[str, tf.Tensor] = {}
      if self.exportable:
          out["logits"] = logits
          return out
  
      if return_model_output or target is None or return_preds:
          prob_map = _bf16_to_float32(tf.math.sigmoid(logits))
  
      if return_model_output:
          out["out_map"] = prob_map
  
      if target is None or return_preds:
          # Post-process boxes (keep only text predictions)
          out["preds"] = [
              dict(zip(self.class_names, preds))
              for preds in self.postprocessor(prob_map.numpy())
          ]
  
      if target is not None:
          thresh_map = self.threshold_head(feat_concat, **kwargs)
          loss = self.compute_loss(logits, thresh_map, target)
          out["loss"] = loss
  
      return out
  
imports: from copy import deepcopy
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import layers
  from tensorflow.keras.applications import ResNet50
  from doctr.file_utils import CLASS_NAME
  from doctr.models.utils import IntermediateLayerGetter, _bf16_to_float32, conv_sequence, load_pretrained_params
  from doctr.utils.repr import NestedObject
  from ...classification import mobilenet_v3_large
  from .base import DBPostProcessor, _DBNet
label_desc: Use image node_id 3 for calling the DBNet obj's underlying member method code with example usage: obj.call(x, target, return_model_output, return_preds) and returns: out, out